A segmentation model to detect cevical lesions based on machine learning of colposcopic images.

Background: Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development. Methods: Between 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer. Results: Experiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively. Conclution: The DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.

Ki-67/MKI67 as a Predictive Biomarker for Clinical Outcome in Gastric Cancer Patients: an Updated Meta-analysis and Systematic Review involving 53 Studies and 7078 Patients.

Gastric cancer (GC) threatens human health worldwide and we performed this meta-analysis to evaluate the clinical value of Ki-67/MKI67 in patients with GC. The combined hazard ratio (HR), odds ratio (OR) and 95% confidence interval (95% CI) were calculated to assess the relationships of Ki-67/MKI67 expression with prognoses and clinicopathological characteristics. Genes co-expressed with MKI67 were collected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses. In total, 53 studies with 7078 patients were included in this study. The pooled HRs indicated that an elevated expression of Ki-67/MKI67 predicted an unfavorable overall survival (HR: 1.54, 95% CI: 1.33-1.78, P<0.0001) and disease-free survival (HR: 2.28, 95% CI: 1.43-3.64, P<0.0001) in GC patients. Additionally, in patients with advanced GC, a high Ki-67/MKI67 expression was also significantly connected with OS (HR: 1.37, 95% CI: 1.18-1.60, P<0.0001). The combined ORs showed that Ki-67/MKI67 expression was related to TNM stage (stage III/IV versus stage I/II: OR=1.93, 95% CI=1.34-2.78, P<0.0001), tumor differentiation (poor versus well/moderate: OR=1.94, 95% CI=1.32-2.85, P=0.001), lymph node metastasis (yes versus no: OR=1.67, 95% CI=1.23-2.25, P=0.001), distant metastasis (yes versus no: OR=1.67, 95% CI=1.24-2.26, P=0.001) and tumor invasion depth (T3/T4 versus Tis/T1/T2: OR=1.98, 95% CI=1.60-2.44, P<0.0001). The results of GO, KEGG pathway and PPI network analyses indicated that Ki-67/MKI67 may be involved in the development of GC via influencing P53 signaling pathway. Ki-67/MKI67 could be a potential indicator to predict the prognosis of patients with GC and identify high-risk cases. Detecting Ki-67/MKI67 expression in clinic may be helpful in optimizing individual treatment and further improving the survival expectancy of patients with GC.

Downregulation of miR­136­5p in hepatocellular carcinoma and its clinicopathological significance.

The clinical significance of microRNA (miR)­136­5p in hepatocellular carcinoma (HCC) has not been verified. Therefore, in the current study, the authors aimed to explore miR­136­5p expression and its clinical significance in HCC, as well as to investigate its potential target genes function. The authors detected the levels of miR­136­5p in 101 pairs of HCC and para­cancer tissues via reverse transcription­quantitative polymerase chain reaction. Gene Expression Omnibus database and the Cancer Genome Atlas (TCGA) database were used to further verify the clinical significance of miR­136­5p expression in HCC. The target genes prediction analysis of miR­136­5p, natural language processing (NLP) analysis of HCC in PubMed and gene functional enrichment analysis were conducted. The miR­136­5p level was markedly downregulated in HCC tissue, compared to para­non­tumor tissue. MiR­136­5p expression decreased in HCC patients with metastasis (P=0.004), advance TNM stage (P<0.001), portal vein tumor embolus (P=0.007) and vaso­invasion (P=0.003), compared with those HCC patients with non­metastasis, early TNM stage, non­portal vein tumor embolus and non­vaso­invasion, respectively. In the TCGA database, downregulated miR­136­5p was also observed in HCC tissue compared to normal liver tissue (P<0.001). There were 178 genes obtained from the overlap between predicted targets and NLP analysis. GO and KEGG pathway analyses revealed some significant pathways related to cancers. Downregulation of miR­136­5p may be responsible for the carcinogenesis and aggressiveness of HCC. miR­136­5p may act as an anti­carcinoma miRNA, which is essential for HCC progression through the regulation of various signaling pathways. Thus, miR­136­5p interaction may provide a novel strategy for HCC treatment.